Autonomous substrate processing system

ABSTRACT

A substrate processing system comprises one or more transfer chambers; a plurality of process chambers connected to the one or more transfer chambers; and a computing device connected to each of the plurality of process chambers. The computing device is to receive first measurements generated by sensors of a first process chamber during or after a process is performed within the first process chamber; determine that the first process chamber is due for maintenance based on processing the first measurements using a first trained machine learning model; after maintenance has been performed on the first process chamber, receive second measurements generated by the sensors during or after a seasoning process is performed within the first process chamber; and determine that the first process chamber is ready to be brought back into service based on processing the second measurements using a second trained machine learning model.

TECHNICAL FIELD

Embodiments of the present disclosure relate to an autonomous orsemi-autonomous substrate processing system, tool and/or processchamber.

BACKGROUND

Traditionally, manufacturing recipes performed by process chambers arestatic recipes that are applied mechanically and without reacting toin-situ conditions. Additionally, determinations of when to performmaintenance on process chambers and when to bring process chambers backinto service are made statically based on set schedules andpredetermined recipes. Process chambers generally do not have anyautonomy or ability to make their own decisions with regards to processrecipes, maintenance, tool qualification, and so on.

SUMMARY

Some of the embodiments described herein cover a substrate processingsystem comprising one or more transfer chambers, a plurality of processchambers connected to the one or more transfer chambers and a computingdevice connected to each of the plurality of process chambers. Theplurality of process chambers include a first process chamber comprisinga first plurality of sensors and a second process chamber comprising asecond plurality of sensors. The computing device is to: receive one ormore first measurements from at least one of the first plurality ofsensors of the first process chamber during or after a first instance ofa process performed within the first process chamber; process the one ormore first measurements using a trained machine learning model, whereinthe trained machine learning model is to generate a first output basedon processing of the one or more first measurements; cause a firstaction to be performed with respect to the first process chamber basedon the first output of the trained machine learning model; determine afirst result of the first action; and update a training of the trainedmachine learning model based on the one or more first measurements, thefirst output, and the first result of the first action.

In some embodiments, a process tool comprises a process chamber, aplurality of sensors connected to the process chamber, and a computingdevice connected to the process chamber and to each of the plurality ofsensors. The computing device is to: receive one or more measurementsfrom at least one of the first plurality of sensors during or after aprocess performed within the process chamber; process the one or moremeasurements using a trained machine learning model, wherein the trainedmachine learning model is to generate an output based on processing ofthe one or more measurements; cause an action to be performed withrespect to the process chamber based on the output of the trainedmachine learning model; determine a result of the action; and update atraining of the trained machine learning model based on the one or moremeasurements, the output, and the result of the action.

In some embodiments, a substrate processing system comprises one or moretransfer chambers, a plurality of process chambers connected to the oneor more transfer chambers, and a computing device connected to each ofthe plurality of process chambers. The plurality of process chambersincludes a first process chamber comprising a first plurality ofsensors. The computing device is to: receive first measurementsgenerated by the first plurality of sensors of the first process chamberduring or after a process is performed within the first process chamber;determine that the first process chamber is due for maintenance based onprocessing the first measurements from the first plurality of sensorsusing a first trained machine learning model; after maintenance has beenperformed on the first process chamber, receive second measurementsgenerated by the plurality of sensors of the first process chamberduring or after a seasoning process is performed within the firstprocess chamber; and determine that the first process chamber is readyto be brought back into service based on processing the secondmeasurements from the plurality of sensors using a second trainedmachine learning model

Numerous other features are provided in accordance with these and otheraspects of the disclosure. Other features and aspects of the presentdisclosure will become more fully apparent from the following detaileddescription, the claims, and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereferences indicate similar elements. It should be noted that differentreferences to “an” or “one” embodiment in this disclosure are notnecessarily to the same embodiment, and such references mean at leastone.

FIG. 1 is a top schematic view of a first example autonomous orsemi-autonomous manufacturing system, according to an embodiment.

FIG. 2 depicts a sectional view of an autonomous or semi-autonomousprocessing chamber, according to an embodiment.

FIG. 3 is a flow chart for a method of automatically making decisionsand performing actions by a process tool and/or substrate processingsystem, according to an embodiment.

FIG. 4 is a flow chart for a method of automatically determining when tostop an etch process, according to an embodiment.

FIG. 5 is a flow chart for a method of automatically determining when toperform maintenance on a process chamber, according to an embodiment.

FIG. 6 is a flow chart for a method of automatically determining when toreturn a process chamber back to service after maintenance has beenperformed, according to an embodiment.

FIG. 7 is a flow chart for a method of making multiple decisionsautonomously by a process tool and/or substrate processing system,according to an embodiment.

FIG. 8 is a flow chart for a method of using a set of sensor data toboth determine when an etch endpoint is reached and to determine whetherto schedule maintenance for an etch process chamber, according to anembodiment.

FIG. 9 is a flow chart for a method of automatically determining when toschedule a process chamber for maintenance and when to return theprocess chamber back to service after maintenance, according to anembodiment.

FIG. 10 illustrates a diagrammatic representation of a machine in theexample form of a computing device within which a set of instructions,for causing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments described herein relate to autonomous and semi-autonomoussubstrate processing systems (e.g., platforms or tool clusters), processtools and process chambers, as well as methods of training suchsubstrate processing systems, tools and/or process chambers and methodsof automatically making decisions by such substrate processing systems,tools and/or process chambers. In embodiments, controllers and/or othercomputing devices for substrate processing systems, process tools and/orprocess chambers include one or more trained machine learning modelsthat are trained to receive sensor measurements from sensors of processchambers and to provide outputs that enable the computing devices tomake decisions as to what actions to perform. Examples of such decisionsinclude decisions to stop an etch process, decisions to schedule aprocess chamber for maintenance, and decisions to stop seasoning aprocess chamber after maintenance has been performed. In embodiments, anautonomous tool or semi-autonomous tool is a tool that can makedecisions locally without data transfer to remote computing devices. Inembodiments, a smart tool (also referred to as an autonomous tool orsemi-autonomous tool) is capable of determining when to stop processes,when to perform maintenance, and when to stop seasoning a processchamber and bring the process chamber back to service after maintenanceis performed on the process chamber. In embodiments, a smart tool is twoor more of determining when to stop an etch process based on an outputof a first machine learning model, determining when to performmaintenance on a process chamber based on an output of a second machinelearning model, and determining when to stop seasoning a process chamberand bring the process chamber back to service based on an output of athird machine learning model.

In embodiments, trained machine learning models are edge-based modelsthat execute on the tools and/or substrate processing systems (e.g.,platforms, transfer chambers, mainframes, factory interfaces, and/ortool clusters) themselves rather than on remote computing devices.Training of the machine learning models may be performed remotely, afterwhich trained machine learning models may be transferred to tools and/orsubstrate processing systems, or may be performed on the tools and/orsubstrate processing systems. Retraining or updating of training of themachine learning models may be performed periodically or continuously onthe tools and/or substrate processing systems. By moving executionand/or training (including retraining) of the machine learning models tothe tools and/or substrate processing systems, latency betweengeneration of sensor measurements and making decisions based on suchsensor measurements can be significantly reduced. This improves anability of the tools and/or substrate processing systems to makereal-time decisions for process chambers. Additionally, moving thedecision making to the tool and/or substrate processing system reducesan amount of data that is transmitted over a network, increasesefficiency, and increases a speed with which decisions can be made. Forexample, a decision of when to stop an etch process can be made withinseconds or fractions of a second from when sensor data that triggerssuch a decision is received in embodiments that include a machinelearning model trained to detect an etch endpoint on the substrateprocessing system or tool.

In another example, a decision of whether to schedule maintenance on aprocess chamber may be made after a process is performed on a firstsubstrate and before the process is performed on a next substrate. Forexample, a decision to take a tool down for service may be made within1-5 minutes of a substrate being processed in a process chamber of thetool, within less than a minute of the substrate being processed in theprocess chamber, or even within a few seconds or fractions of a secondof the substrate being processed in the process chamber. Such quickresponse time reduces an exposure of product substrates (substrates thatwill result in products of devices that will be sold to customers) toprocess chambers that are out of specification and that could causecontamination of the substrates and/or failure of product that isultimately manufactured. In another example, a decision of whether tostop seasoning a process chamber after maintenance has been performed onthat process chamber may be made between seasoning process runs on theprocess chamber. This can reduce an amount of time that it takes torequalify a tool and bring it back into service, reducing an overallcost of ownership of the tool and/or increasing a lifetime throughput ofthe tool. Additionally, dynamically determining when to stop seasoning aprocess chamber reduces an amount of resources (e.g., gases, wafers,etc.) that are used to perform seasoning.

Referring now to the figures, FIG. 1 is a diagram of a cluster tool 100(also referred to as a system, substrate processing system ormanufacturing system) that is configured for substrate fabrication inaccordance with at least some embodiments of the disclosure. The clustertool 100 includes one or more vacuum transfer chambers (VTM) 101, 102, afactory interface 104, a plurality of processing chambers/modules 106,108, 110, 112, 114, 116, and 118, and a platform controller 120. Aserver computing device may also be connected to the cluster tool 100(e.g., to the platform controller 120 of the cluster tool 100). Inembodiments with more than one VTM, such as is shown in FIG. 1 , one ormore pass-through chambers (referred to as vias) may be provided tofacilitate vacuum transfer from one VTM to another VTM. In embodimentsconsistent with that shown in FIG. 1 , two pass-through chambers can beprovided (e.g., pass-through chamber 140 and pass-through chamber 142).

The factory interface 104 includes a loading port 122 that is configuredto receive one or more substrates, for example from a front openingunified pod (FOUP) or other suitable substrate containing box orcarrier, that are to be processed using the cluster tool 100. Theloading port 122 can include one or multiple loading areas 124 a-124 c,which can be used for loading one or more substrates. Three loadingareas are shown. However, greater or fewer loading areas can be used.

The factory interface 104 includes an atmospheric transfer module (ATM)126 that is used to transfer a substrate that has been loaded into theloading port 122. More particularly, the ATM 126 includes one or morerobot arms 128 (shown in phantom) that are configured to transfer thesubstrate from the loading areas 124 a-124 c to the ATM 126, throughdoors 135 (shown in phantom, also referred to as slit valves) thatconnects the ATM 126 to the loading port 122. There is typically onedoor for each loading port (124 a-124 c) to allow substrate transferfrom respective loading port to the ATM 126. The robot arm 128 is alsoconfigured to transfer the substrate from the ATM 126 to load locks 130a, 130 b through doors 132 (shown in phantom, one each for each loadlock) that connect the ATM 126 to the air locks 130 a, 130 b. The numberof load locks can be more or less than two but for illustration purposesonly, two load locks (130 a and 130 b) are shown with each load lockhaving a door to connect it to the ATM 126. Load locks 130 a-b may ormay not be batch load locks.

The load locks 130 a, 130 b, under the control of the platformcontroller 120, can be maintained at either an atmospheric pressureenvironment or a vacuum pressure environment, and serve as anintermediary or temporary holding space for a substrate that is beingtransferred to/from the VTM 101, 102. The VTM 101 includes a robot arm138 (shown in phantom) that is configured to transfer the substrate fromthe load locks 130 a, 130 b to one or more of the plurality ofprocessing chambers 106, 108 (also referred to as process chambers), orto one or more pass-through chambers 140 and 142 (also referred to asvias), without vacuum break, i.e., while maintaining a vacuum pressureenvironment within the VTM 102 and the plurality of processing chambers106, 108 and pass-through chambers 140 and 142. The VTM 102 includes arobot arm 138 (in phantom) that is configured to transfer the substratefrom the air locks 130 a, 130 b to one or more of the plurality ofprocessing chambers 106, 108, 110, 112, 114, 116, and 118, withoutvacuum break, i.e., while maintaining a vacuum pressure environmentwithin the VTM 102 and the plurality of processing chambers 106, 108,110, 112, 114, 116, and 118.

A door 134, e.g., a slit valve door, connects each respective load lock130 a, 130 b, to the VTM 101. Similarly, a door 136, e.g., a slit valvedoor, connects each processing module to the VTM to which the respectiveprocessing module is coupled (e.g., either the VTM 101 or the VTM 102).The plurality of processing chambers 106, 108, 110, 112, 114, 116, and118 are configured to perform one or more processes. Examples ofprocesses that may be performed by one or more of the processingchambers 106, 108, 110, 112, 114, 116, and 118 include cleaningprocesses (e.g., a pre-clean process that removes a surface oxide from asubstrate), anneal processes, deposition processes (e.g., for depositionof a cap layer, a hard mask layer, a barrier layer, a bit line metallayer, a barrier metal layer, etc.), etch processes, and so on. Examplesof deposition processes that may be performed by one or more of theprocess chambers include physical vapor deposition (PVD), chemical vapordeposition (CVD), atomic layer deposition (ALD), and so on. Examples ofetch processes that may be performed by one or more of the processchambers include plasma etch processes.

Platform controller 120 (e.g., a tool and equipment controller) maycontrol various aspects of the cluster tool 100, e.g., gas pressure inthe processing chambers, individual gas flows, spatial flow ratios,plasma power in various process chambers, temperature of various chambercomponents, radio frequency (RF) or electrical state of the processingchambers, and so on. The controller 120 may receive signals from andsend commands to any of the components of the cluster tool 100, such asthe robot arms 128, 138, process chambers 106, 108, 110, 112, 114, 116,and 118, load locks 130 a-b, slit valve doors, and/or one or moresensors, and/or other processing components of the cluster tool 100. Thecontroller 120 may thus control the initiation and cessation ofprocessing, may adjust a deposition rate and/or target layer thickness,may adjust process temperatures, may adjust a type or mix of depositioncomposition, may adjust an etch rate, and the like. The controller 120may further receive and process measurement data (e.g., opticalmeasurement data) from various sensors and make decisions based on suchmeasurement data.

In various embodiments, the controller 120 may be and/or include acomputing device such as a personal computer, a server computer, aprogrammable logic controller (PLC), a microcontroller, and so on. Thecontroller 120 may include (or be) one or more processing devices, whichmay be general-purpose processing devices such as a microprocessor,central processing unit, or the like. More particularly, the processingdevice may be a complex instruction set computing (CISC) microprocessor,reduced instruction set computing (RISC) microprocessor, very longinstruction word (VLIW) microprocessor, or a processor implementingother instruction sets or processors implementing a combination ofinstruction sets. The processing device may also be one or morespecial-purpose processing devices such as an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Thecontroller 120 may include a data storage device (e.g., one or more diskdrives and/or solid state drives), a main memory, a static memory, anetwork interface, and/or other components. The processing device of thecontroller 120 may execute instructions to perform any one or more ofthe methodologies and/or embodiments described herein. The instructionsmay be stored on a computer readable storage medium, which may includethe main memory, static memory, secondary storage and/or processingdevice (during execution of the instructions).

In embodiments, the processing device and memory of controller 120 havean increased capacity as compared to processing power and memory size oftraditional controllers for cluster tools. In embodiments, theprocessing device and memory are sufficient to handle parallel executionand use of multiple trained machine learning models, as well as trainingof the machine learning models. For example, the memory and processingdevice may be sufficient to handle parallel execution of 6-15 differentmachine learning models (e.g., one or more for each of the processchambers 106-108).

In one embodiment, the controller 120 includes an autonomous tool engine121. The autonomous tool engine 121 may be implemented in hardware,firmware, software, or a combination thereof. The autonomous tool engine121 is configured to receive and process measurement data generated byone or more sensors of process chambers 106, 108, 110, 112, 114, 116,and 118 during and/or after execution of processes on the processchambers. The sensor measurements may include temperature measurements,gas flow rates, plasma power measurements, pressure measurements,voltage measurements, current measurements, resistance measurements,time measurements, optical measurements (e.g., such as optical emissionspectrometry measurements and/or reflectometry measurements), positionmeasurements and/or other types of measurements. Some examplemeasurements include a chamber pressure (e.g., which may be measured inmTorr), a nozzle center channel flow rate (e.g., which may be measuredin SCCM), a nozzle middle channel flow rate (e.g., which may be measuredin SCCM), a pressure controller (e.g., valve) position (e.g., which maybe represented as a percentage open such as 30% open), a total gas flowrate into a chamber (e.g., which may be measured in SCCM), inside and/oroutside plasma source currents (e.g., which may be measured in Amps), aplasma source inside to outside current ratio, a source forward power(e.g., which may be measured in Watts), a plasma source matching networkcapacitor position, a plasma source reflected power (e.g., which may bemeasured in Watts), a plasma source series capacitor position (e.g.,which may be represented as a normalized unitless value), a plasmasource shunt capacitor position, an RF bias reflected power (e.g., whichmay be measured in Watts), an RF bias forward poser (e.g., which may bereflected in Watts), an RF source forward power (e.g., which may bemeasured in Watts), an RF source reflected power (e.g., which may bemeasured in Watts), OES spectra measurements for one or more wavelengthsor frequencies (e.g., for wavelengths of 3870 nm, 7035 nm, 775 nm, andso on), a plasma source inside voltage, a plasma source outside voltage,one or more chuck/heater temperatures (e.g., such as an electrostaticchuck inner temperature and an electrostatic chuck outer temperature), ahelium pressure (e.g., for helium delivered to a gap between a substrateand a chuck supporting the substrate), a helium flow rate (e.g., for thehelium delivered to the gap between the substrate and the chuck), a lidtemperature, and so on. Some or all of these measurements may becombined to generate a feature vector that is input into a trainedmachine learning model of the autonomous tool engine 121.

The autonomous tool engine 121 running on platform controller 120 mayinclude one or more trained machine learning models for controllingand/or making decisions for multiple process chambers 106, 108, 110,112, 114, 116, 118. The one or more trained machine learning models havebeen trained to receive sensor measurements from and/or associated witha process chamber 106, 108, 110, 112, 114, 116, 118 and to make aprediction, classification or determination about the process chamber ora process to be performed or being performed by the process chamber.Each of the trained machine learning models may be associated with adifferent decision making process for a process chamber 106, 108, 110,112, 114, 116, 118.

In one embodiment, one or more of the trained machine learning models isa regression model trained using regression. Examples of regressionmodels are regression models trained using linear regression or Gaussianregression. A regression model predicts a value of Y given known valuesof X variables. The regression model may be trained using regressionanalysis, which may include interpolation and/or extrapolation. In oneembodiment, parameters of the regression model are estimated using leastsquares. Alternatively, Bayesian linear regression, percentageregression, leas absolute deviations, nonparametric regression, scenariooptimization and/or distance metric learning may be performed to trainthe regression model.

In one embodiment, one or more of the trained machine learning modelsare decision trees, random forests, support vector machines, or othertypes of machine learning models.

In one embodiment, one or more of the trained machine learning models isan artificial neural network (also referred to simply as a neuralnetwork). The artificial neural network may be, for example, aconvolutional neural network (CNN) or a deep neural network. In oneembodiment, processing logic performs supervised machine learning totrain the neural network.

Artificial neural networks generally include a feature representationcomponent with a classifier or regression layers that map features to atarget output space. A convolutional neural network (CNN), for example,hosts multiple layers of convolutional filters. Pooling is performed,and non-linearities may be addressed, at lower layers, on top of which amulti-layer perceptron is commonly appended, mapping top layer featuresextracted by the convolutional layers to decisions (e.g. classificationoutputs). The neural network may be a deep network with multiple hiddenlayers or a shallow network with zero or a few (e.g., 1-2) hiddenlayers. Deep learning is a class of machine learning algorithms that usea cascade of multiple layers of nonlinear processing units for featureextraction and transformation. Each successive layer uses the outputfrom the previous layer as input. Neural networks may learn in asupervised (e.g., classification) and/or unsupervised (e.g., patternanalysis) manner. Some neural networks (e.g., such as deep neuralnetworks) include a hierarchy of layers, where the different layerslearn different levels of representations that correspond to differentlevels of abstraction. In deep learning, each level learns to transformits input data into a slightly more abstract and compositerepresentation.

One of more of the trained machine learning models may be recurrentneural networks (RNNs). An RNN is a type of neural network that includesa memory to enable the neural network to capture temporal dependencies.An RNN is able to learn input-output mappings that depend on both acurrent input and past inputs. The RNN will address past and futuremeasurements and make predictions based on this continuous measurementinformation. For example, sensor measurements may continually be takenduring a process, and those sets of measurements may be input into theRNN sequentially. Current sensor measurements and prior sensormeasurements may affect a current output of the trained machine learningmodel. One type of RNN that may be used is a long short term memory(LSTM) neural network.

Some trained machine learning models of the autonomous tool engine 121may be used for multiple different process chambers that have a commonprocess chamber type and that are used to perform the same or similarprocesses. For example, process chamber 106 and process chamber 108 mayboth be etch chambers that perform a same etch process. A trainedmachine learning model may be used to determine when to schedule each ofprocess chamber 106 and process chamber 108 for maintenance.

Some trained machine learning models may be specific to a particularprocess chamber 106, 108, 110, 112, 114, 116, 118. For such trainedmachine learning models, a different instance of the trained machinelearning model may have been trained for each of the process chambers106, 108, 110, 112, 114, 116, 118. For example, the autonomous toolengine 121 may include a first trained machine learning model fordetermining when to schedule maintenance for process chamber 106, asecond trained machine learning model for determining when to schedulemaintenance for process chamber 108, a third trained machine learningmodel for determining when to schedule maintenance for process chamber110, a fourth trained machine learning model for determining when toschedule maintenance for process chamber 112, a fifth trained machinelearning model for determining when to schedule maintenance for processchamber 114, a sixth trained machine learning model for determining whento schedule maintenance for process chamber 116, and a seventh trainedmachine learning model for determining when to schedule maintenance forprocess chamber 118.

Some trained machine learning models of autonomous tool engine 121 useall sensor measurements generated by a process chamber and/or for aprocess chamber (e.g., for a process performed on the process chamber).Some trained machine learning models of autonomous tool engine 121 use asubset of generated sensor measurements. For example, a trained machinelearning model trained to determine an etch endpoint may receive as aninput measurements from one or more optical sensors, such as areflectometry signal and/or an optical emission spectroscopy signal.

In one embodiment, autonomous tool engine 121 includes a maintenancemanager 123. Maintenance manager 123 includes one or more trainedmachine learning models that are trained to receive sensor measurementsof a process chamber and/or of a process from sensors associated withthe process chamber and to output a decision as to whether or notmaintenance should be performed on the process chamber. In oneembodiment, maintenance manager 123 includes a different trained machinelearning model for each process chamber 106, 108, 110, 112, 114, 116,118 of cluster tool 100. In one embodiment, maintenance manager 123includes a different trained machine learning model for each type ofprocess chamber. In one embodiment, maintenance manager 123 includes adifferent trained machine learning model for each type of processchamber that performs a particular process. For example, processchambers 106, 108, 110 and 112 may be the same type of process chamber,where process chambers 106, 108 perform a first process and processchambers 110, 112 perform a second process. Accordingly, maintenancemanager 123 may include a first trained machine learning model forprocess chambers 106, 108 and a second trained machine learning modelfor process chambers 110, 112.

In one embodiment, autonomous tool engine 121 includes a requalificationmanager 125. Requalification manager 125 includes one or more trainedmachine learning models that are trained to receive sensor measurementsof a process chamber and/or of a process from sensors associated withthe process chamber and to output a decision as to whether or not theprocess chamber is properly seasoned and ready to return to service. Thesensor measurements may be received during and/or after a seasoningprocess is performed in a process chamber. The seasoning process may beperformed while a blanket substrate, a sensor substrate, a baresubstrate or a test substrate is in the process chamber in embodiments.In one embodiment, requalification manager 125 includes a differenttrained machine learning model for each process chamber 106, 108, 110,112, 114, 116, 118 of cluster tool 100. In one embodiment,requalification manager 125 includes a different trained machinelearning model for each type of process chamber. In one embodiment,requalification manager 125 includes a different trained machinelearning model for each type of process chamber that performs aparticular process. For example, process chambers 106, 108, 110 and 112may be the same type of process chamber, where process chambers 106, 108perform a first process and process chambers 110, 112 perform a secondprocess. Accordingly, requalification manager 125 may include a firsttrained machine learning model for process chambers 106, 108 and asecond trained machine learning model for process chambers 110, 112.

In one embodiment, autonomous tool engine 121 includes a process manager127. Process manager 127 includes one or more trained machine learningmodels that are trained to receive sensor measurements of a processchamber and/or of a process from sensors associated with the processchamber and to output a decision as to whether or not particular processconditions are met, such as whether or not an etch endpoint has beenreached. In one embodiment, process manager 127 includes a differenttrained machine learning model for each process chamber 106, 108, 110,112, 114, 116, 118 of cluster tool 100. In one embodiment, processmanager 127 includes a different trained machine learning model for eachtype of process chamber. In one embodiment, process manager 127 includesa different trained machine learning model for each type of processchamber that performs a particular process. For example, processchambers 106, 108, 110 and 112 may be the same type of process chamber,where process chambers 106, 108 perform a first process and processchambers 110, 112 perform a second process. Accordingly, process manager127 may include a first trained machine learning model for processchambers 106, 108 and a second trained machine learning model forprocess chambers 110, 112.

In embodiments, inputs to at least one of the machine learning models ofprocess manager 127 are feature vectors including one or more sensormeasurements from one or more points in time during a manufacturingprocess, and the outputs of the machine learning model are etch endpointdecisions (e.g., indicating that an etch endpoint is reached or that anetch endpoint is not reached), estimated film thickness values and/orestimated trench depths. In one embodiment, a trained neural network istrained to receive reflectometry measurements of deposited layers on asubstrate and/or optical emissions spectroscopy measurements generatedduring etching of a deposited layer on a substrate as an input and tooutput at least one of an estimated film thickness and/or a trenchdepth. The estimated film thickness and/or trench depth may then becompared to a target film thickness and/or trench depth by processmanager 127. The target film thickness and/or trench depth may beincluded in a process recipe for etching the film. If the estimated filmthickness equals the target film thickness and/or the estimated trenchdepth equals the target trench depth, then process manager 127determines that an etch endpoint is reached.

The trained machine learning model may have been trained using atraining dataset including multiple data items that each include spectra(e.g., reflectometry measurements of films having particular thicknessesand/or trenches with particular depths) of films that are generated fromoptical sensors such as OES sensors and/or reflectometry sensors duringa process (e.g., during a deposition process or etch process) andassociated thickness values and/or depth values measured after theprocess (e.g., optical critical dimension (OCD) measurements). A machinelearning model such as a neural network (e.g., a convolutional neuralnetwork) or a regression model (e.g., a Gaussian regression model or alinear regression model) may be trained to correlate the optical sensormeasurements (e.g., spectra information) to film thickness and/or trenchdepth. The trained machine learning model can then receive opticalsensor measurements (e.g., spectra information) during a process (e.g.,an etch or deposition process), and estimate a film thickness and/ortrench depth based on the optical sensor measurements.

In one embodiment, training the machine learning model includesperforming principal component analysis to determine a set of spectrainformation that has a largest impact on film thickness and/or trenchdepth. For example, through principal component analysis, the system maydetermine that y (trench depth or film thickness) is a function of x₁,x₂, through x_(n) (where x_(i) for i from 1 to n are principalcomponents of the spectra information (e.g., different wavelengths)), asset forth in the equation below:y=F(α_(i) x _(i))for i=1 to n, where α_(i) are weights for the respective principalcomponents x_(i).

In one embodiment, the trained machine learning model processes opticalsensor measurements periodically (e.g., every 50-100 milliseconds)during a process such as an etch process. For each input, the trainedmachine learning model may output a film thickness and/or trench depth.The process manager 127 may compare the estimated trench depth and/orfilm thickness to a target trench depth and/or film thickness todetermine whether an etch endpoint has been reached or will be reachedbefore the next sensor measurements are processed. In one embodiment,process manager 127 determines an estimated etch rate based on multiplefilm thickness and/or trench depth estimates and the associated times atwhich measurements associated with the film thickness and/or trenchdepth estimates were generated. For example, the formula(D₁−D₂)/(T₂−T₁)=R may be used to determine etch rate, where D₁ is thethickness at time T₁, D₂ is the thickness at time T₂ and R is the etchrate. Thickness values over a last few measurements may therefore beexamined to estimate etch rate. The estimated etch rate may then beextrapolated into the future to estimate when an etch endpoint will bereached. Processing logic may include information identifying how longit takes to process optical sensor measurements to determine a filmthickness and/or trench depth. This information may be compared to theestimated time at which an etch endpoint is predicted to be reached. Ifthe estimated trench endpoint will be reached before a next set ofoptical measurements can be processed by the trained machine learningmodel, then the time at which the estimated trench endpoint will bereached may be used to determine when to stop the etch process. In oneembodiment, the process manager 127 uses 2-10 estimates of filmthickness and/or trench depth based on previous optical sensormeasurements to determine an etch rate and extrapolates the etch rateinto the future to predict the next 2-10 data points (i.e., theestimated film thickness and/or trench depth to be output by the trainedmachine learning model for the next 2-10 sets of optical measurements).In one embodiment, the trained machine learning model is a recurrentneural network (RNN). In one embodiment, the trained machine learningmodel is a neural network (e.g., a CNN) that receives static spectrainformation. In one embodiment the trained machine learning model is alinear regression model and in another embodiment the machine learningmodel is a Gaussian regression model. In one embodiment, the trainedmachine learning model is a random forest.

In embodiments, the sensor measurements (e.g., OES and/or reflectometryspectra information) are correlated to actual OCD information ratherthan to a yes/no decision as to whether an etch endpoint has beenreached. Correlating the sensor measurements to actual OCD informationhas been shown to increase accuracy by 20-30% over correlating merely toetch endpoint decisions.

Controller 120 may be operatively connected to a server (not shown). Theserver may be or include a computing device that operates as a factoryfloor server that interfaces with some or all tools in a fabricationfacility. The server may perform training to generate the trainedmachine learning models, and may send the trained machine learningmodels to autonomous tool engine 121 on platform controller 120.Alternatively, the machine learning models may be trained on platformcontroller 120.

Training of a neural network may be achieved in a supervised learningmanner, which involves feeding a training dataset consisting of labeledinputs through the network, observing its outputs, defining an error (bymeasuring the difference between the outputs and the label values), andusing techniques such as deep gradient descent and backpropagation totune the weights of the network across all its layers and nodes suchthat the error is minimized. In many applications, repeating thisprocess across the many labeled inputs in the training dataset yields anetwork that can produce correct output when presented with inputs thatare different than the ones present in the training dataset. Inhigh-dimensional settings, such as large images, this generalization isachieved when a sufficiently large and diverse training dataset is madeavailable.

Each of the trained machine learning models of the autonomous toolengine 121 may be periodically or continuously retrained to achievecontinuous learning and improvement of the trained machine learningmodels. Each model may generate an output based on an input, an actionmay be performed based on the output, and a result of the action may bemeasured. In some instances the result of the action is measured withinfractions of a second (e.g., milliseconds), seconds or minutes, and insome instances it takes longer to measure the result of the action. Forexample, one or more additional processes may be performed before aresult of the action can be measured. The action and the result of theaction may indicate whether the output was a correct output and/or adifference between what the output should have been and what the outputwas. Accordingly, the action and the result of the action may be used todetermine a target output that can be used as a label for the sensormeasurements. Once the result of the action is determined, the input(i.e., sensor measurements), the output of the trained machine learningmodel, and the target output of the machine learning model (or theaction and the result of the action) may be used as a new training dataitem. The new training data item may then be used to further train thetrained machine learning model. This retraining process may be performedon-tool by the autonomous tool engine 121 of the platform controller120.

In one embodiment, process manager 127 includes one or more trainedmachine learning models that have been trained to detect etch endpoints,film thickness and/or etch depth. Such trained machine learning modelstrained to detect etch endpoints, film thickness and/or trench depth maybe trained from a training dataset including optical measurements (e.g.,reflectometry measurements and/or optical emission spectroscopymeasurements) and labels indicating film thickness and/or trench depth,as discussed above. In one embodiment, optical measurements providespectra information, which may be correlated to depth or thicknessinformation and/or to an etch endpoint. The spectra information, whichmay include reflectometry information and/or optical emissionspectroscopy information, may then be input into a trained machinelearning model to produce a thickness or depth (e.g., trench depth)output. The thickness or depth output may be compared to a targetthickness or depth to determine whether an etch endpoint has beenreached. A similar process may be performed to measure targetthicknesses of films during deposition processes. For example, a trainedmachine learning model of process manager 127 may use opticalmeasurements to determine when a film being deposited has reached atarget thickness, and a deposition process may be stopped when the filmreaches the target thickness.

Different etch endpoint detection machine learning models may be trainedfor each etch recipe and/or for each process chamber. Once the trainedmachine learning model is employed by platform controller 120 (e.g., byprocess manager 127), optical measurements may be periodically orcontinuously generated by optical sensors of an etch chamber during anetch process. These optical measurements may be processed by a trainedmachine learning model of process manager 127 to determine when an etchendpoint has been reached and when to stop an etch process (or a step inan etch process). Afterwards, thickness or depth measurements and/orother optical critical dimension (OCD) measurements may be performed ofa substrate having a film that was etched by the etch process. The OCDmeasurements may be performed, for example, using normal-incidencespectroscopic ellipsometry, optical scatterometry, scanning electronmicroscopy, and/or other OCD measurement techniques. The measuredthickness or depth may be compared to a target thickness or depth, and adifference may be used along with the output of the machine learningmodel and the sensor measurements that were input into the machinelearning model to update a training of the machine learning model. Inone embodiment, a training data item including the sensor measurements,a predicted OCD and an actual measured OCD are used to further train themachine learning model. The trained machine learning model may beretrained after every lot or after every substrate is processed. Forexample, after every 25 wafers the machine learning model may beupdated, and that updated machine learning model, which has increasedaccuracy and reflects a current condition of a process chamber, may beused for processing the next 25 wafers. This provides very accuratedepth control for etch processes, and can improve yield by about0.2-0.3% in embodiments.

In one embodiment, requalification manager 125 includes one or moretrained machine learning models that have been trained to detect when aprocess chamber has recovered (e.g., when a process chamber is ready toreturn to service and start processing product substrates again) afterpreventative maintenance or other maintenance was performed on theprocess chamber. Such trained machine learning models trained to detectrecovery from maintenance may be trained from a training datasetincluding many different measurements generated by one or more processchambers during seasoning processes. The many different measurements mayinclude optical measurements of a substrate (e.g., reflectometrymeasurements and/or optical emission spectroscopy measurements),pressure measurements, power measurements, voltage measurements, currentmeasurements, other electrical measurements, temperature measurements,and so on generated during a seasoning process, and labels indicatingwhether or not the process chamber was ready to return to service afterthe seasoning process at which the combined sensor measurements weretaken was complete.

Different maintenance recovery detection machine learning models may betrained (e.g., using a supervised learning or semi-supervised learningprocess) for each process chamber and/or for each pair of a processchamber and a process or set of processes performed on that processchamber. Once the trained machine learning model is employed by platformcontroller 120 (e.g., by requalification manager 125), sensormeasurements may be periodically (e.g., every 10 seconds, every 30seconds, every minute, etc.) or continuously generated by multiplesensors of a process chamber during and/or after a seasoning process.These measurements may be processed by a trained machine learning modelof requalification manager 125 to determine when enough seasoningprocesses have been performed on the process chamber after a maintenanceevent, and thus when the process chamber is ready to return to service.Additionally, these measurements may be processed by the trained machinelearning model to determine whether to end a current seasoning process.Afterwards, one or more test process may be performed on the processchamber, and a result of the test process(es) may be that the processchamber is requalified or that the process chamber is not requalified(indicating that more seasoning processes should be run on the processchamber). In one embodiment, the test processes that may be performedinclude one or more of a blanket wafer etch process in which a blanketwafer etch rate and/or etch uniformity are measured, a patterned waferetch process in which a patterned wafer etch rate and/or etch uniformityare measured and/or a particle test process in which a particle testwafer is processed and then particles are counted on the particle testwafer. If the blanket wafer etch rate and/or etch uniformity are withintolerance of a target blanket wafer etch rate and a target blanket waferetch uniformity, the patterned wafer etch rate and/or etch uniformityare within tolerance of a target patterned wafer etch rate and a targetpatterned wafer etch uniformity and/or the particle count is withintolerance of a target particle count, then the process chamber may berequalified. In one embodiment, the target particle count is fewer thana threshold number of particles of a particular size or larger. Forexample, the target particle count could be fewer than five particlesthat are 22 nm in diameter or larger.

In one embodiment, a training data item including the sensormeasurements, a prediction as to whether the process chamber is ready toreturn to service and machine learning output as to whether the processchamber was actually ready to return to service (e.g., an indicationthat the process chamber passed a requalification test or did not pass arequalification test) is used to update a training of the trainedmachine learning model. The trained machine learning model may beretrained each time after the process chamber (or after other processchambers) is returned to service after being taken down for maintenance.Embodiments reduce the number of repetitions of a seasoning process thatare performed prior to bringing a process chamber back into serviceafter maintenance. For example, a standard process for seasoning an etchchamber may be to run 25 iterations of a seasoning process on the etchchamber, and to then perform a test process on the process chamber.However, in embodiments processing logic may determine immediately whenthe process chamber is ready to have a test process run rather thanwaiting until a full 25 iterations of the seasoning process have beencompleted. In some embodiments, no test process is run after the trainedmachine learning model has indicated that a process chamber is ready toreturn to service.

In one embodiment, a trained machine learning model of requalificationmanager 125 is trained to output a chamber condition index (CCI) valuefor a process chamber based on sensor measurements input during aseasoning process. The chamber condition index (CCI) value may becompared to a threshold CCI value, and if the CCI value output by thetrained machine learning model meets or exceeds the threshold CCI value,then the requalification manager 125 may determine that the processchamber is ready to return to service. If the predicted CCI value doesnot meet the CCI threshold, then further seasoning processes may beperformed. Requalification manager 125 may also determine how close theprocess chamber is to being ready for service based on a differencebetween the predicted CCI value and the CCI threshold.

In embodiments, the CCI value for a process chamber may be based on oneor more etch rate values (e.g., mean etch rate values) and/or etchuniformity values measured based on a blanket wafer etch process and/ora patterned wafer etch process. The CCI value may also be based on aparticle count of a particle wafer processed by a process chamber afterseasoning is performed. The CCI value for a chamber may be measured byperforming one or more requalification tests after seasoning isperformed on a process chamber. The CCI value may, in some embodiments,be a normalized value that is correlated to blanket wafer etch rate,blanket wafer etch uniformity, patterned wafer etch rate, patternedwafer etch uniformity and/or particle count. A CCI value of 1 mayrepresent test results that show a target blanket wafer etch rate, atarget blanket wafer etch uniformity, a target patterned wafer etchrate, a target patterned wafer etch uniformity and/or a target particlecount. A CCI value of less than 1 may indicate a deviation from one ormore of the target blanket wafer etch rate, the target blanket waferetch uniformity, the target patterned wafer etch rate, the targetpatterned wafer etch uniformity and/or the target particle count. In oneembodiment, a CCI threshold is some value (e.g., 0.9) that represents anacceptable combined deviation from the target blanket wafer etch rate,the target blanket wafer etch uniformity, the target patterned waferetch rate, the target patterned wafer etch uniformity and/or the targetparticle count.

A requalification test may be performed on a process chamber that hasbeen identified as ready to return to service (e.g., after the trainedmachine learning model outputs a predicted CCI value that is greaterthan a CCI threshold), and a result of the test may be an actualmeasured CCI value. The requalification test may include performing a anetch process on a blanket wafer (e.g., a wafer with a blanket or uniformfilm of an oxide or nitride film) and measuring a blanket wafer etchrate and/or a blanket wafer etch uniformity. The requalification testmay additionally or alternatively include performing an etch process ona patterned wafer and measuring a patterned wafer etch rate and/or apatterned wafer etch uniformity. The requalification test mayadditionally or alternatively include performing a process (e.g., anetch process) on a bare wafer or blanket wafer, and counting particleson the wafer. An actual CCI may be computed based on the blanket waferetch rate, patterned wafer etch rate and/or particle count. If theactual measured chamber condition index value meets or exceeds thethreshold, then the process chamber may be returned to service. Themachine learning model may then be updated using a data point comprisingthe sensor measurements, the predicted chamber condition index value andthe actual chamber condition index value.

In one embodiment, maintenance manager 123 includes one or more trainedmachine learning models that have been trained to detect whenmaintenance should be performed on a process chamber. Such trainedmachine learning models trained to detect when a process chamber is duefor maintenance may be trained from a training dataset including manydifferent measurements generated by one or more process chambers duringprocesses performed on product substrates (e.g., on product wafers). Themany different measurements may include optical measurements of asubstrate (e.g., reflectometry measurements and/or optical emissionspectroscopy measurements), pressure measurements, power measurements(e.g., bias power, source power, plasma power, etc.), voltagemeasurements, current measurements, other electrical measurements,temperature measurements, and so on generated during a process, andlabels indicating whether or not the process chamber was due formaintenance after the process at which the combined sensor measurementswere taken was complete. In embodiments, the sensor measurements includeup to or about 165 different sensor measurements, each takenperiodically during a process. Additionally, occasionally a test processmay be run using a test substrate, blanket substrate (substrate with auniform coating that is not patterned), bare substrate, sensor substrate(substrate with multiple sensors disposed thereon), etc. Sensormeasurements from the process chamber (and optionally from the sensorsubstrate) may be generated and input into the trained machine learningmodel to generate an output.

Different maintenance prediction machine learning models may be trainedfor each process chamber and/or for each pair of a process chamber and aprocess or set of processes performed on that process chamber. Once thetrained machine learning model is employed by platform controller 120(e.g., by maintenance manager 123), sensor measurements may beperiodically or continuously generated by multiple sensors of a processchamber (and/or sensor substrate) during a product process and/or anoccasional test process. These measurements may be processed by atrained machine learning model of maintenance manager 123 to determinewhen the process chamber warrants maintenance, and thus when the processchamber should be taken down for maintenance. Examples of maintenanceinclude cleaning the process chamber, replacing one or more parts of theprocess chamber, and so on. In embodiments, the maintenance predictionmachine learning models identify a type of maintenance that should beperformed on the process chamber based on the sensor measurements. Forexample, a trained machine learning model may indicate that a processchamber should be cleaned, that a protective liner should be replaced,that a process kit ring should be replaced, that a showerhead should bereplaced, and so on.

In one embodiment, a machine learning model of maintenance manager 123is trained to receive sensor measurements as inputs and to output achamber condition index (CCI) or chamber process condition index (CPCI)for each substrate processed by a chamber. One or more metrologymeasurements may later be performed on the substrate (optionally afterone or more additional processes are performed on the substrate todetermine one or more OCDs of the substrate or devices on the substrate.The OCDs may correlate to the CCI value or the CPCI value determined forthat substrate based on the performed process. If OCD measurements forthe substrate deviate from target OCD values by more than a thresholdamount, then this may indicate that maintenance should have beenperformed on the process chamber that performed the process. A data itemmay be generated based on the sensor measurements generated during theprocess, the CCI or CPCI value output by the trained machine learningmodel, and a) an indication as to whether the measured OCD valuesdeviated from target OCD values and/or an amount that the measured OCDvalues deviated from the target OCD values and/or b) an indication as towhether maintenance should have been performed.

After a process chamber has been marked as due for being serviced, atechnician may determine whether the process chamber actually should beserviced and/or a type of maintenance that should be performed on theprocess chamber. In one embodiment, a training data item including thesensor measurements, a prediction as to whether the process chamber isdue for maintenance (and/or a type of maintenance to be performed) andan indication as to whether maintenance was actually warranted for theprocess chamber is used to update a training of the trained machinelearning model. The trained machine learning model may be retrained eachtime after the process chamber (or after other process chambers) isscheduled for maintenance. Alternatively, or additionally, the machinelearning model may be continuously or periodically retrained using datapoints associated with substrates processed by a process chamber, wherethe data points include sensor measurements, CCI or CPCI values,differences between target OCD values and measured OCD values and/orindications as to whether maintenance should have been performed.Embodiments reduce the number of substrates that get processed by aprocess chamber that needs to be serviced, and additionally ensures thatprocess chambers are not serviced more frequently than is called for.

In one embodiment, a trained machine learning model of maintenancemanager 123 is trained to output a CCI value or CPCI value for a processchamber based on sensor measurements input during a manufacturingprocess. The CCI or CPCI value may be compared to a threshold CCI orCPCI value, and if the CCI or CPCI value output by the trained machinelearning model is below the threshold CCI or CPCI value (or if theoutput CCI or CPCI value is above the threshold in some embodiments),then the maintenance manager 123 may determine that the process chambershould be taken down for maintenance. If the CCI or CPCI value is abovethe CCI or CPCI threshold (or if the output CCI or CPCI value is belowthe threshold in some embodiments), then further manufacturing processesmay be performed in the process chamber without first schedulingmaintenance for the process chamber. Maintenance manager 123 may alsodetermine how close the process chamber is to being due for maintenancebased on a difference between the predicted CCI or CPCI value and theCCI or CPCI threshold.

In various embodiments, the server may be and/or include a computingdevice such as a personal computer, a server computer, a programmablelogic controller (PLC), a microcontroller, and so on. The server mayinclude (or be) one or more processing devices, which may begeneral-purpose processing devices such as a microprocessor, centralprocessing unit, or the like. More particularly, the processing devicemay be a complex instruction set computing (CISC) microprocessor,reduced instruction set computing (RISC) microprocessor, very longinstruction word (VLIW) microprocessor, or a processor implementingother instruction sets or processors implementing a combination ofinstruction sets. The processing device may also be one or morespecial-purpose processing devices such as an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Theserver may include a data storage device (e.g., one or more disk drivesand/or solid state drives), a main memory, a static memory, a networkinterface, and/or other components. The processing device of the servermay execute instructions to train machine learning models and to sendthe trained machine learning models to platform controllers 120 and/orto controllers of individual tools (e.g., controllers of processchambers) in embodiments. The instructions may be stored on a computerreadable storage medium, which may include the main memory, staticmemory, secondary storage and/or processing device (during execution ofthe instructions).

In some instances, one or more processes may be performed on a substratein a first cluster tool (e.g., cluster tool 100). Measurements fromthese processes may be input into one or more trained machine learningmodels, which may then generate outputs. Actions may be performed basedon the outputs, and results of the actions may be determined ormeasured. The measurements, outputs, actions and/or results may then beused to update a training of the machine learning model(s) (e.g., themachine learning models of maintenance manager 123, requalificationmanager 125 and/or process manager 127). The updating of the trainedmachine learning models may be performed on the server or on theplatform controller 120. The updated versions of the trained machinelearning model(s) may be sent to the server, which may propagate theupdated versions of the trained machine learning models to other clustertools that have similar process chambers and/or that perform similarprocesses.

FIG. 2 is a sectional view of a semiconductor processing tool 200including a process chamber 202 and a chamber controller 205 operativelyconnected to the process chamber 202. Chamber controller 205 may bemounted to the process chamber 230, or may be disposed near the processchamber (e.g., connected to another component of a substrate processingsystem). The process chamber 202 may be an etch process chamber, adeposition chamber, an anneal chamber, or other type of process chamberused to process substrates (e.g., wafers) such as semiconductorsubstrates. For example, the processing chamber 202 may be a chamber fora plasma etcher or plasma etch reactor, a plasma cleaner, a CVD or ALDreactor (e.g., such as a plasma enhanced CVD or ALD reactor), an ionassisted deposition (IAD) chamber, a physical vapor deposition (PVD)chamber, and so forth.

In one embodiment, the processing chamber 202 includes a chamber bodyand a showerhead 230 that encloses an interior volume 206. Theshowerhead 230 may include a showerhead base and a showerhead gasdistribution plate. Alternatively, the showerhead 230 may be replaced bya lid and a nozzle in some embodiments, or by multiple pie shapedshowerhead compartments and plasma generation units in otherembodiments. The chamber body may be fabricated from aluminum, stainlesssteel or other suitable material such as titanium (Ti). The chamber bodygenerally includes sidewalls 208 and a bottom 210. A liner 216 may bedisposed adjacent the sidewalls 208 to protect the chamber body.

An exhaust port 226 may be defined in the chamber body, and may couplethe interior volume 206 to a pump system 228. The pump system 228 mayinclude one or more pumps and throttle valves utilized to evacuate andregulate the pressure of the interior volume 206 of the processingchamber 202.

The showerhead 230 (or lid) may be supported on the sidewalls 208 of thechamber body. The showerhead 230 (or lid) may be opened to allow accessto the interior volume 206 of the processing chamber 202, and mayprovide a seal for the processing chamber 202 while closed. A gas panel258 may be coupled to the processing chamber 202 to provide processand/or cleaning gases to the interior volume 206 through the showerhead230 or lid and nozzle. Showerhead 230 may be used for processingchambers used for dielectric etch (etching of dielectric materials). Theshowerhead 230 may include a gas distribution plate (GDP) and may havemultiple gas delivery holes 232 throughout the GDP. The showerhead 230may include the GDP bonded to an aluminum base or an anodized aluminumbase. The GDP may be made from Si or SiC, or may be a ceramic which iscoated with Y₂O₃, Al₂O₃, Y₃Al₅O₁₂ (YAG), and so forth.

For processing chambers used for conductor etch (etching of conductivematerials), a lid may be used rather than a showerhead. The lid mayinclude a center nozzle that fits into a center hole of the lid. The lidmay be a coated ceramic component coated with Al₂O₃, Y₂O₃, YAG, or aceramic compound comprising Y₄Al₂O₉ and a solid-solution of Y₂O₃—ZrO₂.The nozzle may also be a ceramic, coated with Y₂O₃, YAG, or the ceramiccompound comprising Y₄Al₂O₉ and a solid-solution of Y₂O₃—ZrO₂.

Examples of processing gases that may be used to process substrates inthe processing chamber 200 include halogen-containing gases, such asC₂F₆, SF₆, SiCl₄, HBr, NF₃, CF₄, CHF₃, CH₂F₃, F, NF₃, Cl₂, CCl₄, BCl₃and SiF₄, among others, and other gases such as O₂, or N₂O. Examples ofcarrier gases include N₂, He, Ar, and other gases inert to process gases(e.g., non-reactive gases).

A heater assembly 248 is disposed in the interior volume 206 of theprocessing chamber 202 below the showerhead 230 or lid. The heaterassembly 248 includes a support 250 that holds a substrate 244 duringprocessing. The support 250 is attached to the end of a shaft 252 thatis coupled to the chamber body via a flange. The support 250, shaft 252and flange may be constructed of a material containing AlN, for example.The support 250 may further include mesas (e.g., dimples or bumps). Thesupport may additionally include wires, for example, tungsten wires (notshown), embedded within the heater material of the support 250. In oneembodiment, the support 250 may include metallic heater and sensorlayers that are sandwiched between AlN ceramic layers. Such an assemblymay be sintered in a high-temperature furnace to create a monolithicassembly. The layers may include a combination of heater circuits,sensor elements, ground planes, radio frequency grids and metallic andceramic flow channels.

Exemplary chamber components of the process chamber 202 include, withoutlimitations, an electrostatic chuck, a nozzle, a gas distribution plate,a shower head (e.g., 230), an electrostatic chuck component, a chamberwall (e.g., 208), a liner (e.g., 216), a liner kit, a gas line, achamber lid, a nozzle, a single ring, a processing kit ring, edge ring,a base, a shield, a plasma screen, a flow equalizer, a cooling base, achamber viewport, a bellow, any part of a heater assembly (including thesupport 250, the shaft 252, the flange), faceplate, blocker plate, andso on.

In embodiments, process chamber includes many different sensors 235,203, 230. The sensors may include optical sensors, such as opticalemission spectrometer 230 and/or reflectometer 203. The sensors 235 mayadditionally include thermal sensors, pressure sensors, power sensors,other electrical sensors, flow rate sensors, and so on. Some sensors 235may be internal to the process chamber 202, while other sensors 235 maybe external to the process chamber 202 and measure the flow and/ordelivery of gases, power, etc. to the process chamber 230. In oneembodiment, sensors 235 generate about 165 different sensor measurementsfor process chamber 202.

Chamber controller 205 may serve the same or a similar function asplatform controller 120, but may be configured to perform operations forone or a few process chambers (e.g., for process chamber 202). Forexample, chamber controller 205 may be configured to control etchchambers of a cluster tool, or etch chambers that perform a particularetch process. In embodiments, chamber controller 205 includes anautonomous tool engine 221, which may include a maintenance manager 223,a requalification manager 225 and/or a process manager 227. Therequalification manager 225, process manager 227, maintenance manager223 and autonomous tool engine 221 may correspond to the like namedcomponents of FIG. 1 in embodiments. For a single platform with multipleprocess chambers attached thereto, each of the process chamber mayinclude its own dedicated chamber controller 205. Alternatively, some ofthe process chambers attached to a cluster tool or mainframe may share acommon chamber controller. In one embodiment, chamber controller 205 isnot used, and instead a platform controller 120 is used to control allof the process chambers attached to a cluster tool.

Process chamber 202 may include one or more windows or viewports 220,240. The windows or viewports may be quartz, glass, sapphire, diamond,silicon carbide, a transparent crystal, or an optically transparentceramic, for example. In one embodiment, the process chamber 202includes a window 220 in a lid, nozzle or showerhead 230, and furtherincludes a viewport 240 in a sidewall 216.

In embodiments, a reflectometer 203 is coupled to the window 220. Thereflectometer 203 includes a light source 201 (e.g., a broadband lightsource or other source of electromagnetic radiation), a light couplingdevice 204 (e.g., a collimator or a mirror), and a spectrometer 225. Thelight source 201 and spectrometer 225 may be optically coupled to thelight coupling device 204 through one or more fiber optic cable 232.

In various embodiments, the light coupling device 204 may be adapted tocollimate or otherwise transmit light in two directions along an opticalpath. A first direction may include light from the light source 201 thatis to be collimated and transmitted into the chamber 206 through thewindow 220. A second direction may be reflected light that has reflectedoff of a substrate 244 and back through the window 220 that passes backinto the light coupling device 204. The reflected light may be focusedinto the fiber optic cable 232 and thus directed to the spectrometer 225in the second direction along the optical path. Further, the fiber opticcable 232 may be coupled between the spectrometer 225 and the lightsource 201 for efficient transfer of light between the light source 201,to the window 220, and back to the spectrometer 225.

In an embodiment, the light source emits light at a spectrum of about200-800 nm, and the spectrometer 225 also has a 200-800 nm wavelengthrange. The spectrometer 225 may be adapted to detect a spectrum of thereflected light received from the light coupling device 204, e.g., thelight that has reflected off of a substrate in chamber 202 and backthrough the window 220 and been focused by the light coupling device 204into the fiber optic cable 232.

In one embodiment, the controller 205 may direct the light source 201 toflash on and then receive a light spectrum from the spectrometer 225.The controller 205 may also keep the light source off and receive asecond spectrum from the spectrometer 225 when the light source 201 isoff. The controller 205 may subtract the second spectrum from the firstspectrum to determine the reflectometry signal for a moment in time. Thecontroller 205 may then mathematically fit the reflectometry signal toone or more thin film models to determine one or more optical thin filmproperty of a film that is measured.

In some embodiments, the one or more optical thin film property mayinclude film thickness, a refractive index (n), and/or an extinctioncoefficient (k) value. The refractive index is the ratio of the speed oflight in a vacuum to the speed of light in the film. The extinctioncoefficient is a measure of how much light is absorbed in the film. Thecontroller 205 may determine, using the n and k values, a composition ofthe film. The autonomous tool engine 221 may be configured to analyzethe data of the one or more property of the film, determine a thicknessof the film and/or determine a depth of a trench etched in the film.

In one embodiment, optical emission spectrometer (OES) 230 is connectedto process chamber 202 via viewport 240. OES 230 may direct light intointerior volume 206 of process chamber 202 and/or may measure light fromthe interior volume 206 in order to perform optical emissionspectroscopy using the light. Flashes of light may be directed onto aplasma in the interior volume 206 of the process chamber 202 by the OES230. The OES 230 may then receive spectra information associated withbulk plasma conditions in the chamber. The received spectra informationmay comprise information associated with the concentration of etchreactants and etch byproducts in the plasma. In particular, the spectrainformation may be associated with a ratio of etch reactants to etchbyproducts. The ratio of the etch reactants to etch byproducts maychange dramatically when an etch endpoint is reached (e.g., when a layeris fully etched and there is no more material from the layer to beetched). The autonomous tool engine 221 be configured to analyze thespectra measured from the interior volume 206 of the process chamber 202to determine whether an etch endpoint has been reached, and/or todetermine a thickness of a film, to determine one or more properties ofthe film, and/or to determine a depth of a trench etched in the film.The OES data and the reflectometry data may both be used to determinesuch properties and/or conditions in embodiments.

In some embodiments, autonomous tool engine 221 uses sensor measurementsfrom reflectometer 203, OES 230 and one or more of sensors 235 to makedecisions with respect to process chamber 202. Chamber controller 205may determine, for example, whether maintenance is due for processchamber 202, a type of maintenance to be performed on process chamber202, whether process chamber 202 is ready to be brought back intoservice after undergoing maintenance and seasoning, and so on usingautonomous tool engine 221.

FIGS. 3-9 are flow charts for methods of training machine learningmodels and/or using trained machine learning models to make decisionsfor process chambers based on sensor measurements, according toembodiments. The methods may be performed with the components describedwith reference to FIGS. 1-2 , as will be apparent. For example, methodsmay be performed by platform controller 120 or chamber controller 205 inembodiments. At least some operations of the methods may be performed bya processing logic that may comprise hardware (e.g., circuitry,dedicated logic, programmable logic, microcode, etc.), software (e.g.,instructions run on a processing device to perform hardware simulation),or a combination thereof. Although shown in a particular sequence ororder, unless otherwise specified, the order of the processes can bemodified. Thus, the illustrated embodiments should be understood only asexamples, and the illustrated processes can be performed in a differentorder, and some processes can be performed in parallel. Additionally,one or more processes can be omitted in various embodiments. Thus, notall processes are performed in every embodiment. Other process flows arepossible.

FIG. 3 is a flow chart for a method 300 of automatically makingdecisions and performing actions by a process tool and/or substrateprocessing system, according to an embodiment. At block 302 of method300, processing logic causes a processing chamber to perform a process,such as an etch process, a deposition process, a test process, or aseasoning process. At block 305, processing logic receives measurementsfrom one or more sensors of the process chamber during and/or after theprocess. At block 310, processing logic processes the measurement ormeasurements using a trained machine learning model. Based on theprocessing of the measurement(s), the trained machine learning modelgenerates an output. The output may be an trench depth, a filmthickness, an indication as to whether an etch endpoint has beenreached, an indication as to whether the process chamber should bescheduled for maintenance or an indication as to whether the processchamber should be returned to service. The trained machine learningmodel may have been trained as set forth herein above, and maycorrespond to any of the trained machine learning models set forthherein above.

At block 315, processing logic determines that the output satisfies oneor more criteria. The criteria may include a trench depth criterion, achamber condition index threshold, a yes/no criterion, or some othercriterion. In the case of a trained machine learning model trained todetect an etch endpoint, the criterion may be a trench depth, and thecriterion may be satisfied if a determined trench depth is equal to orgreater than a target trench depth. In one embodiment, the trainedmachine learning model outputs a yes or a no, where a yes indicates thatan etch endpoint has been reached and a no indicates that the etchendpoint has not yet been reached. In the case of a trained machinelearning model trained to determine whether maintenance should beperformed on a process chamber, an output may be a CCI or CPCI value,and a criterion may be satisfied if the determined CCI or CPCI valueoutput by the trained machine learning model is below a CCI or CPCIthreshold. In one embodiment, the trained machine learning model outputsa yes or a no, where a yes indicates that maintenance should beperformed on the process chamber.

In one embodiment, the trained machine learning model outputs multiplemaintenance classifications, and for each maintenance classification thetrained machine learning model provides a yes indicating that the typeof maintenance associated with that maintenance classification should beperformed or a no indicating that the type of maintenance associatedwith that maintenance classification need not be performed. Examples ofmaintenance classifications include scheduled cleaning, part replacementfor a first part, part replacement for a second part, and so on. In thecase of a trained machine learning model trained to determine whether aprocess chamber that has undergone maintenance is ready to be returnedto service after one or more seasoning processes have been performed onthe process chamber, an output may be a CCI or CPCI value, and acriterion may be satisfied if the determined CCI or CPCI value output bythe trained machine learning model is at or above a CCI or CPCIthreshold. In one embodiment, the trained machine learning model outputsa yes or a no, where a yes indicates that no more seasoning is warrantedfor the process chamber (and that the process chamber is ready to returnto service) and no indicates that one or more seasoning processes shouldstill be performed on the process chamber (and that the process chamberis not ready to return to service).

At block 320, processing logic causes an action to be performed withrespect to the process chamber based on the output of the machinelearning model and whether the output satisfies the one or morecriteria. With respect to a trained machine learning model trained todetect an etch endpoint, the action may be to stop an etch process. Withrespect to a trained machine learning model trained to detect when aprocess chamber should undergo maintenance, the action may be to flagthe process chamber for maintenance, to take the process chamber out ofservice so that further product substrates are not processed by theprocess chamber and/or to schedule maintenance for the process chamber.With respect to a trained machine learning model trained to detect whena process chamber that has undergone maintenance and for which one ormore iterations of a seasoning process have been performed on theprocess chamber, the action may be to place the process chamber backinto service, to mark the process chamber are ready for a qualificationtest to be performed and/or to schedule a requalification test orprocess for the process chamber.

At block 325, processing logic determines a result of the action. In thecase of a trained machine learning model trained to detect an etchendpoint, the result may be one or more optical critical dimensionmeasurements, such as an optically measured trench depth. In the case ofa trained machine learning model trained to automatically determine whena process chamber should undergo maintenance, a result of the action maybe an indication from a technician as to whether maintenance waswarranted or measured OCD values of substrates and/or an indication asto whether the measured OCD values are above or below one or morethresholds (e.g., deviate from target OCD values by more than athreshold amount). In the case of a trained machine learning modeltrained to automatically determine when a process that has undergonemaintenance is ready to return to service, a result of the action may bea requalification test result, which may be obtained by performing arequalification process with a blanket wafer, test wafer, sensor wafer,bare wafer or other wafer in the process chamber and measuring one ormore properties and/or conditions of the blanket wafer, test wafer,sensor wafer, bare wafer or other wafer and/or sensor measurements ofthe process chamber during and/or after the requalification process.Examples of wafer measurements include particle count, metalcontamination, etch depth, layer thickness, and so on. In oneembodiment, a result of the action is an indication that the processchamber passed or did not pass requalification.

At block 330, processing logic updates a training of the trained machinelearning model based on the sensor measurements, the output of themachine learning model, and the result of the action. Accordingly,continuous learning may be performed to continuously update and improvethe trained machine learning model. This enables the trained machinelearning model to adapt to chamber conditions on an ongoing bases. Theretraining of the trained machine learning model may be performedon-tool on a controller at which the trained machine learning model isdeployed in embodiments.

FIG. 4 is a flow chart for a method 400 of automatically determiningwhen to stop an etch process, according to an embodiment. At block 402of method 400, processing logic initiates an etch process in an etchchamber. The etch process may be performed on a product substrate havingone or more films thereon. At block 405, processing logic receives oneor more measurements from one or more optical sensors of the processchamber during and/or after the etch process. The optical sensors mayinclude, for example, a reflectometry sensor and/or an optical emissionsspectroscopy sensor. At block 410, processing logic processes themeasurements using a trained machine learning model that has beentrained to detect a trench depth, a film thickness and/or an etchendpoint condition. The trained machine learning model may have beentrained to generate an output that indicates a trench depth and/or afilm thickness and/or an output that indicates whether an etch endpointhas been reached.

At block 415, processing logic determines whether the output of thetrained machine learning model satisfies an etch endpoint criterion. Inone embodiment, processing logic compares an output trench depth with atarget trench depth and/or an output film thickness with a target filmthickness. In one embodiment, processing logic determines whether theoutput is an indication that an etch endpoint has been reached (e.g.,which may be a yes/no output). If the output trench depth is less thanthe target trench depth, or if the film thickness is greater than thetarget film thickness, or if the output is an indication that an etchendpoint has not been reached, then the etch process continues and themethod returns to block 405, at which additional sensor measurements aregenerated. If the output trench depth is equal to or greater than thetarget trench depth, or if the output is an indication that an etchendpoint has been reached, then the method continues to block 420.

At block 420, processing logic determines that the etch endpoint hasbeen reached. At block 425, processing logic stops the etch process (ora step in the etch process). At block 430, processing logic determines acritical dimension (e.g., a trench depth) of the film that was etchedduring the etch process. The critical dimension may be determined duringor after a downstream process in a manufacturing sequence for thesubstrate having the film. At block 435, processing logic determines adifference between the measured critical dimension of the film and atarget critical dimension of the film (e.g., a difference between atarget thickness or trench depth and a measured thickness or trenchdepth). At block 440, processing logic updates the training of themachine learning model based on the measurements received at block 405,the output from block 410 indicating that the etch endpoint was reached,and the difference between the measured critical dimension and thetarget critical dimension of the film. Accordingly, continuous learningmay be performed to continuously update and improve the trained machinelearning model. The retraining of the trained machine learning model maybe performed on-tool on a controller at which the trained machinelearning model is deployed in embodiments.

FIG. 5 is a flow chart for a method 500 of automatically determiningwhen to perform maintenance on a process chamber, according to anembodiment. At block 502 of method 500, processing logic initiatesprocess on a product substrate in chamber. The process may be an etchprocess, a deposition process, an anneal process, or some other process,for example. The process may be performed on a product substrate havingone or more films thereon and/or may be performed to deposit a filmthereon. At block 505, processing logic receives one or moremeasurements from a set of sensors of the process chamber during and/orafter the process. At block 510, processing logic processes themeasurements using a trained machine learning model that has beentrained to determine whether maintenance should be performed on theprocess chamber. The trained machine learning model may have beentrained to generate an output that indicates a CCI value or a CPCI valueand/or an output that indicates whether maintenance is due and/or a typeof maintenance to be performed.

At block 515, processing logic determines whether the output of thetrained machine learning model satisfies criterion. In one embodiment,processing logic compares an output CCI with a CCI or CPCI threshold. Ifthe CCI or CPCI value is below the CCI or CPCI threshold, thenprocessing logic may determine that the output satisfies the criterion.If the CCI or CPCI value is above the CCI or CPCI threshold, then thecriterion may not be satisfied. In one embodiment, the output of thetrained machine learning model is a yes/no indication as to whethermaintenance should be performed. If the output is a yes, thatmaintenance should be performed (or that a particular type ofmaintenance should be performed), the criterion is satisfied. If theoutput is a no, that maintenance should not be performed, then thecriterion is not satisfied. If the criterion is not satisfied, themethod continues to block 520. If the criterion is satisfied, the methodproceeds to block 525.

At block 520, processing logic initiates the process on a new substrate(after causing a robot arm to remove the first substrate from theprocess chamber and to insert the new substrate into the processchamber). The method then returns to block 505 and sensor measurementsassociated with performance of the process on the new substrate arereceived. Additionally, the method may proceed to block 535.

At block 525, processing logic determines that the process chamber isdue for maintenance. At block 530, processing logic may flag the processchamber for maintenance (e.g., a cleaning) and/or may actively schedulea cleaning for the process chamber. At block 535, processing logic mayreceive an indication as to whether maintenance was actually performedon the process chamber. Processing logic may additionally oralternatively receive an indication as to a state of the process chamberand/or critical dimension measurements of product substrates processedby the process chamber at block 502 and/or block 520. At block 540,processing logic updates the training of the machine learning modelbased on the measurements received at block 505, the output from block510 indicating whether maintenance should be performed, and at least oneof the indication as to whether maintenance was performed and/or adifference between measured critical dimension(s) and the targetcritical dimension(s) of one or more film on the product substrate.Accordingly, continuous learning may be performed to continuously updateand improve the trained machine learning model. The retraining of thetrained machine learning model may be performed on-tool on a controllerat which the trained machine learning model is deployed in embodiments.

FIG. 6 is a flow chart for a method 600 of automatically determiningwhen to return a process chamber back to service after maintenance hasbeen performed, according to an embodiment. At block 602 of method 600,processing logic initiates seasoning process in a chamber. The seasoningprocess is a chamber conditioning process that causes a state of theprocess chamber to reach a known state. Proper seasoning or conditioningof a process chamber after maintenance (e.g., after a part replacementand/or after a cleaning process such as a wet clean process or a dryclean process) improves wafer-to-wafer process repeatability. In oneembodiment, the seasoning process cause passivation of reactor surfacesby plasma generated species, which can change the reactive stickingcoefficients of radicals. Chamber seasoning may be performed to ensurethat critical dimensions of devices are consistently reproduced byenabling a uniform plasma with the same ion density, electrontemperature, and fluxes to be repeated wafer-to-wafer. The process maybe performed on a blanket substrate, bare substrate, test substrate,etc.

At block 605, processing logic receives one or more measurements from aset of sensors of the process chamber during and/or after the process.At block 610, processing logic processes the measurements using atrained machine learning model that has been trained to determinewhether seasoning is complete and/or whether a process chamber is readyto be returned to service. The trained machine learning model may havebeen trained to generate an output that indicates an estimated CCIand/or an output that indicates whether seasoning is complete (and thatthe process chamber can be returned to service).

At block 615, processing logic determines whether the output of thetrained machine learning model satisfies a criterion. In one embodiment,processing logic compares an output estimated CCI with a CCI threshold.If the estimated CCI is at or above the CCI threshold, then processinglogic may determine that the output satisfies the criterion. If theestimated CCI is below the CCI threshold, then the criterion may not besatisfied. In one embodiment, the output of the trained machine learningmodel is a yes/no indication as to whether seasoning is complete. If theoutput is a no, that seasoning is not complete, the criterion is notsatisfied. If the output is a yes, that seasoning is complete, then thecriterion is satisfied. If the criterion is not satisfied, the methodcontinues to block 620. If the criterion is satisfied, the methodproceeds to block 625.

At block 620, processing logic initiates another iteration of theseasoning process, optionally on a new substrate (after causing a robotarm to remove the first substrate from the process chamber and to insertthe new substrate into the process chamber). The method then returns toblock 605 and sensor measurements associated with performance of theprocess on the new substrate are received.

At block 625, processing logic determines that the process chamber isready to be requalified and/or is ready to return to service (to be usedon product substrates). At block 630, processing logic may flag theprocess chamber for qualification and/or may schedule a requalificationprocess. At block 635, processing logic may receive an indication as towhether the process chamber passed the requalification test. Theindication may include one or more measurement results of one or moretest substrate that was processed using a test recipe or test process.In one embodiment, a blanket wafer etch process is performed on ablanket wafer, a patterned wafer etch process is performed on apatterned wafer and/or a particle test process is performed on aparticle wafer (e.g., which may be a blank wafer or a blanket wafer).From the blanket wafer etch process a mean blanket wafer etch rate and ablanket wafer etch uniformity may be measured. From the patterned waferetch process a mean patterned wafer etch rate and a patterned wafer etchuniformity may be measured. After the particle test, particles may becounted on the particle wafer. The measurement results may include, forexample, an on-wafer particle count, metal contamination, filmthickness, film composition, blanket wafer etch rate, blanket wafer etchuniformity, patterned wafer etch rate, patterned wafer etch uniformity,and so on. Processing logic may additionally or alternatively receive anindication as to a state of the process chamber. An actual CCI value maybe determined for the process chamber based on the measurement results.

At block 640, processing logic updates the training of the machinelearning model based on the measurements received at block 605, theoutput from block 610 indicating whether maintenance should beperformed, and the indication as to whether the process chamber passedrequalification tests and/or results of requalification tests.Accordingly, continuous learning may be performed to continuously updateand improve the trained machine learning model. The retraining of thetrained machine learning model may be performed on-tool on a controllerat which the trained machine learning model is deployed in embodiments.

FIG. 7 is a flow chart for a method 700 of making multiple decisionsautonomously by a process tool and/or substrate processing system,according to an embodiment. At block 702 of method 700, processing logiccauses a processing chamber to perform a first process, such as an etchprocess, a deposition process, a test process, or a seasoning process.At block 705, processing logic receives first measurements from one ormore sensors of the process chamber during and/or after the process. Atblock 710, processing logic processes the first measurement ormeasurements (or a first subset of the first measurements) using a firsttrained machine learning model such as a machine learning model trainedto perform etch endpoint detection. Based on the processing of themeasurement(s), the first trained machine learning model generates anoutput. The output may be an trench depth, a film thickness, anindication as to whether an etch endpoint has been reached, anindication as to whether the process chamber should be scheduled formaintenance or an indication as to whether the process chamber should bereturned to service. The first trained machine learning model may havebeen trained as set forth herein above, and may correspond to any of thetrained machine learning models set forth herein above.

At block 715, processing logic determines that the first outputsatisfies one or more first criteria. The first criteria may include atrench depth criterion, a chamber condition index threshold, a yes/nocriterion, or some other criterion. Processing logic causes a firstaction to be performed with respect to the process chamber based on thefirst output of the first trained machine learning model satisfying afirst criterion.

At block 720, processing logic processes the first measurement ormeasurements (or a second subset of the first measurements) using asecond trained machine learning model. Based on the processing of themeasurement(s), the second trained machine learning model generates asecond output. The second output may be a trench depth, a filmthickness, an indication as to whether an etch endpoint has beenreached, an indication as to whether the process chamber should bescheduled for maintenance or an indication as to whether the processchamber should be returned to service. The second trained machinelearning model may have been trained as set forth herein above, and maycorrespond to any of the trained machine learning models set forthherein above.

At block 725, processing logic determines that the second outputsatisfies one or more second criteria (which are different from thefirst criteria). The second criteria may include a trench depthcriterion, a film thickness criterion, a chamber condition indexthreshold, a yes/no criterion, or some other criterion. Processing logiccauses a second action to be performed with respect to the processchamber based on the second output of the second trained machinelearning model satisfying a second criterion.

At block 730, processing logic causes the processing chamber to performa second process, such as an etch process, a deposition process, a testprocess, or a seasoning process. The second process may be differentfrom or the same as the first process performed at block 702. Forexample, the first process may be an etch process performed on a productsubstrate and the second process may be a chamber seasoning processperformed after maintenance was scheduled on the process chamber. Atblock 735, processing logic receives second measurements from the one ormore sensors of the process chamber during and/or after the secondprocess. At block 740, processing logic processes the second measurementor measurements (or a subset of the second measurements) using a thirdtrained machine learning model such as a machine learning model trainedto detect when seasoning of a process chamber is complete. Based on theprocessing of the second measurement(s), the third trained machinelearning model generates a third output. The third output may be antrench depth, a film thickness, an indication as to whether an etchendpoint has been reached, an indication as to whether the processchamber should be scheduled for maintenance or an indication as towhether the process chamber should be returned to service. The thirdtrained machine learning model may have been trained as set forth hereinabove, and may correspond to any of the trained machine learning modelsset forth herein above.

At block 745, processing logic determines that the third outputsatisfies one or more third criteria. The third criteria may include atrench depth criterion, a chamber condition index threshold, a yes/nocriterion, or some other criterion. Processing logic causes a thirdaction to be performed with respect to the process chamber based on thethird output of the third trained machine learning model satisfying athird criterion.

At block 750, processing logic receives results of the first action,second action and/or third action. At block 755, processing logicupdates a training of the first trained machine learning model, thesecond trained machine learning model and/or the third trained machinelearning model based on the results of the first, second and thirdactions, the first, second and third outputs, and the first and secondmeasurements, respectively.

FIG. 8 is a flow chart for a method 800 of using a set of sensor data toboth determine when an etch endpoint is reached and to determine whetherto schedule maintenance for an etch process chamber, according to anembodiment. At block 802 of method 800, processing logic initiates anetch process in an etch chamber. The etch process may be performed on aproduct substrate having one or more films thereon. At block 805,processing logic receives one or more measurements from one or moreoptical sensors of the process chamber during and/or after the etchprocess. At block 810, processing logic processes the measurements (or afirst subset of the measurements such as one or more opticalmeasurements from the received measurements) using a first trainedmachine learning model that has been trained to detect a trench depth, afilm thickness and/or an etch endpoint condition. The trained machinelearning model may have been trained to generate an output thatindicates a trench depth, a film thickness and/or an output thatindicates whether an etch endpoint has been reached.

At block 815, processing logic determines whether the output of thetrained machine learning model satisfies an etch endpoint criterion. Ifthe output fails to satisfy the trench endpoint criterion the methodreturns to block 805, at which additional sensor measurements aregenerated. If the output satisfies the trench endpoint criterion, thenthe method continues to block 820.

At block 820, processing logic determines that the etch endpoint hasbeen reached. At block 825, processing logic stops the etch process (ora step in the etch process).

At block 830, processing logic processes the measurements (or a secondsubset of the measurements) using a second trained machine learningmodel that has been trained to determine whether maintenance should beperformed on the process chamber. The second trained machine learningmodel may have been trained to generate an output that indicates a CCIand/or an output that indicates whether maintenance is due and/or a typeof maintenance to be performed.

At block 835, processing logic determines whether the output of thetrained machine learning model satisfies a second criterion. If theoutput satisfies the second criterion, the method proceeds to block 840.If the output fails to satisfy the second criterion, the method returnsto block 802 and the etch process is performed on a new substrate.

At block 840, processing logic determines that the process chamber isdue for maintenance. At block 845, processing logic may flag the processchamber for maintenance (e.g., a cleaning) and/or may actively schedulea cleaning for the process chamber. At block 850, processing logic mayreceive an indication as to whether maintenance was actually performedon the process chamber.

Processing logic may update a training of both the first trained machinelearning model and the second trained machine learning model based onthe sensor measurements, the respective outputs, and indications as towhether the respective outputs were correct outputs.

FIG. 9 is a flow chart for a method 900 of automatically determiningwhen to schedule a process chamber for maintenance and when to returnthe process chamber back to service after maintenance, according to anembodiment. At block 902 of method 900, processing logic initiatesprocess on a product substrate in a process chamber. The process may bean etch process, a deposition process, an anneal process, or some otherprocess, for example. The process may be performed on a productsubstrate having one or more films thereon and/or may be performed todeposit a film thereon. At block 905, processing logic receives firstmeasurements from a set of sensors of the process chamber during and/orafter the first process. At block 910, processing logic processes thefirst measurements using a first trained machine learning model that hasbeen trained to determine whether maintenance should be performed on theprocess chamber. The trained machine learning model may have beentrained to generate an output that indicates a CCI and/or an output thatindicates whether maintenance is due and/or a type of maintenance to beperformed.

At block 915, processing logic determines whether the first output ofthe first trained machine learning model satisfies one or more firstcriteria (e.g., whether an output CCI value is below a CCI threshold).If the first output satisfies the one or more first criteria, the methodcontinues to block 920. If the first output fails to satisfy the one ormore first criteria, the method returns to block 902 and the firstprocess is performed on a new substrate in the process chamber.

At block 920, processing logic determines that the process chamber isdue for maintenance. At block 925, processing logic may flag the processchamber for maintenance (e.g., a cleaning) and/or may actively schedulea cleaning for the process chamber.

At block 930, after maintenance has been performed on the processchamber, processing logic may initiate a seasoning process for theprocess chamber. At block 935, processing logic receives secondmeasurements from the set of sensors of the process chamber duringand/or after the seasoning process. At block 940, processing logicprocesses the second measurements using a second trained machinelearning model that has been trained to determine whether seasoning iscomplete and/or whether the process chamber is ready to be returned toservice. The second trained machine learning model may have been trainedto generate a second output that indicates a CCI and/or a second outputthat indicates whether seasoning is complete (and that the processchamber can be returned to service).

At block 945, processing logic determines whether the second output ofthe trained machine learning model satisfies one or more secondcriteria. In one embodiment, processing logic compares an output CCIwith a CCI threshold. If the CCI is at or above the CCI threshold, thenprocessing logic may determine that the output satisfies the secondcriterion. If the CCI is below the CCI threshold, then the secondcriterion may not be satisfied. If the second criterion is notsatisfied, the method continues and returns block 930 and anotherseasoning process is performed on the process chamber. If the criterionis satisfied, the method proceeds to block 950.

At block 950, processing logic determines that seasoning is finished andthat the process chamber is ready to be requalified and/or is ready toreturn to service (to be used on product substrates). At block 955,processing logic may flag the process chamber for qualification and/ormay schedule a requalification process. At block 960, processing logicmay receive an indication as to whether the process chamber passed arequalification test. Processing logic may additionally or alternativelyreceive an indication as to a state of the process chamber. If theprocess chamber failed to pass the requalification test, the methodreturns to block 930 and another seasoning process is performed on theprocess chamber. If the process chamber passed the requalification test,the method continues to block 965. At block 965, processing logic bringsthe process back into service. Processing logic may also update atraining of the first and/or second trained machine learning models asdiscussed herein.

FIG. 10 illustrates a diagrammatic representation of a machine in theexample form of a computing device 1000 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeembodiments, the machine may be connected (e.g., networked) to othermachines in a Local Area Network (LAN), an intranet, an extranet, or theInternet. The machine may operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet computer, a set-topbox (STB), a Personal Digital Assistant (PDA), a cellular telephone, aweb appliance, a server, a network router, switch or bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines (e.g., computers)that individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methodologies discussedherein.

The example computing device 1000 includes a processing device 1002, amain memory 1004 (e.g., read-only memory (ROM), flash memory, dynamicrandom access memory (DRAM) such as synchronous DRAM (SDRAM) or RambusDRAM (RDRAM), etc.), a static memory 1006 (e.g., flash memory, staticrandom access memory (SRAM), hard disk (magnetic storage) etc.), and asecondary memory (e.g., a data storage device 1018), which communicatewith each other via a bus 1030.

Processing device 1002 represents one or more general-purpose processorssuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processing device 1002 may be a complex instructionset computing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,processor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 1002may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. Processing device 1002 is configured to execute theprocessing logic (instructions 1022) for performing the operations andsteps discussed herein.

The computing device 1000 may further include a network interface device1008. The computing device 1000 also may include a video display unit1010 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)),an alphanumeric input device 1012 (e.g., a keyboard), a cursor controldevice 1014 (e.g., a mouse), and a signal generation device 1016 (e.g.,a speaker).

The data storage device 1018 may include a machine-readable storagemedium (or more specifically a computer-readable storage medium) 1028 onwhich is stored one or more sets of instructions 1022 embodying any oneor more of the methodologies or functions described herein. Theinstructions 1022 may also reside, completely or at least partially,within the main memory 1004 and/or within the processing device 1002during execution thereof by the computer system 1000, the main memory1004 and the processing device 1002 also constituting computer-readablestorage media.

The computer-readable storage medium 1028 may also be used to store anautonomous tool engine 121, and/or a software library containing methodsthat call an autonomous tool engine 121. While the computer-readablestorage medium 1028 is shown in an example embodiment to be a singlemedium, the term “computer-readable storage medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more sets of instructions. The term “computer-readablestorage medium” shall also be taken to include any medium that iscapable of storing or encoding a set of instructions for execution bythe machine and that cause the machine to perform any one or more of themethodologies described herein. The term “computer-readable storagemedium” shall accordingly be taken to include, but not be limited to,non-transitory computer readable media such as solid-state memories, andoptical and magnetic media.

The modules, components and other features described herein (for examplein relation to FIGS. 1-2 ) can be implemented as discrete hardwarecomponents or integrated in the functionality of hardware componentssuch as ASICS, FPGAs, DSPs or similar devices. In addition, the modulescan be implemented as firmware or functional circuitry within hardwaredevices. Further, the modules can be implemented in any combination ofhardware devices and software components, or only in software.

Some portions of the detailed description have been presented in termsof algorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a targetresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, as apparent from the followingdiscussion, it is appreciated that throughout the description,discussions utilizing terms such as “receiving”, “identifying”,“determining”, “selecting”, “providing”, “storing”, or the like, referto the actions and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system's registersand memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

Embodiments of the present invention also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the discussed purposes, or it may comprise a generalpurpose computer system selectively programmed by a computer programstored in the computer system. Such a computer program may be stored ina computer readable storage medium, such as, but not limited to, anytype of disk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic disk storage media, opticalstorage media, flash memory devices, other type of machine-accessiblestorage media, or any type of media suitable for storing electronicinstructions, each coupled to a computer system bus.

The preceding description sets forth numerous specific details such asexamples of specific systems, components, methods, and so forth in orderto provide a good understanding of several embodiments of the presentdisclosure. It will be apparent to one skilled in the art, however, thatat least some embodiments of the present disclosure may be practicedwithout these specific details. In other instances, well-knowncomponents or methods are not described in detail or are presented insimple block diagram format in order to avoid unnecessarily obscuringthe present disclosure. Thus, the specific details set forth are merelyexemplary. Particular implementations may vary from these exemplarydetails and still be contemplated to be within the scope of the presentdisclosure.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment” in various places throughout thisspecification are not necessarily all referring to the same embodiment.In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” When the term “about” or “approximately” is usedherein, this is intended to mean that the nominal value presented isprecise within ±10%.

Although the operations of the methods herein are shown and described ina particular order, the order of operations of each method may bealtered so that certain operations may be performed in an inverse orderso that certain operations may be performed, at least in part,concurrently with other operations. In another embodiment, instructionsor sub-operations of distinct operations may be in an intermittentand/or alternating manner.

It is understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the disclosure should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A substrate processing system, comprising: one ormore transfer chambers; a plurality of process chambers connected to theone or more transfer chambers, the plurality of process chamberscomprising a first process chamber comprising a first plurality ofsensors and a second process chamber comprising a second plurality ofsensors; and a computing device connected to each of the plurality ofprocess chambers, wherein the computing device is to: receive one ormore first measurements from at least one of the first plurality ofsensors of the first process chamber during or after a first instance ofa seasoning process performed within the first process chamber afterperforming maintenance on the first process chamber, wherein the one ormore first measurements comprise a first set of measurements from thefirst plurality of sensors generated during the first instance of theseasoning process; process the one or more first measurements using atrained machine learning model, wherein the trained machine learningmodel is to generate a first output based on processing of the one ormore first measurements, wherein the first output comprises anindication that the first process chamber is ready to be brought backinto service; cause a first action to be performed with respect to thefirst process chamber based on the first output of the trained machinelearning model; determine a first result of the first action; and updatea training of the trained machine learning model based on the one ormore first measurements, the first output, and the first result of thefirst action.
 2. The substrate processing system of claim 1, wherein theplurality of process chambers are configured to perform the seasoningprocess.
 3. The substrate processing system of claim 1, wherein thecomputing device is further to: receive one or more second measurementsfrom at least one of the second plurality of sensors of the secondprocess chamber during or after a second instance of the seasoningprocess performed within the second process chamber; process the one ormore second measurements using the trained machine learning model togenerate a second output; cause a second action to be performed withrespect to the second process chamber based on the second output of thetrained machine learning model; determine a second result of the secondaction; and update the training of the trained machine learning modelbased on the one or more second measurements, the second output, and thesecond result of the second action.
 4. The substrate processing systemof claim 1, further comprising: a factory interface connected to the oneor more transfer chambers via one or more load lock; wherein thecomputing device is an on-tool computing device that is attached to atleast one of a transfer chamber of the one or more transfer chambers, aprocess chamber of the plurality of process chambers, or the factoryinterface.
 5. The substrate processing system of claim 1, wherein thefirst action comprises a test process to be run on a test substrate inthe first process chamber, and wherein the first result of the firstaction comprises one or more measurements of the test substrategenerated during or after the test process.
 6. The substrate processingsystem of claim 1, wherein the first set of measurements compriseoptical measurements, power measurements, and pressure measurements. 7.The substrate processing system of claim 1, wherein the trained machinelearning model comprises a neural network.
 8. A substrate processingsystem, comprising: one or more transfer chambers; a plurality ofprocess chambers connected to the one or more transfer chambers, theplurality of process chambers comprising a first etch chamber comprisinga first plurality of sensors and a second process chamber comprising asecond plurality of sensors; and a computing device connected to each ofthe plurality of process chambers, wherein the computing device is to:receive one or more first measurements from at least one of the firstplurality of sensors of the first etch chamber during or after a firstinstance of an etch process performed within the first etch chamber,wherein the one or more first measurements comprise a reflectometrymeasurement of a film on a substrate generated during the first instanceof the etch process; process the one or more first measurements using atrained machine learning model, wherein the trained machine learningmodel is to generate a first output based on processing of the one ormore first measurements, wherein the first output comprises at least oneof an estimated film thickness or an estimated trench depth of the film;cause a first action to be performed with respect to the first etchchamber based on the first output of the trained machine learning model,wherein the first action comprises stopping the etch process; determinea first result of the first action, wherein the first result of thefirst action comprises at least one of a) a difference between ameasured thickness of the film and the estimated film thickness of thefilm or b) a difference between a measured trench depth of the film andthe estimated trench depth of the film; and update a training of thetrained machine learning model based on the one or more firstmeasurements, the first output, and the first result of the firstaction.
 9. A process tool, comprising: a process chamber, wherein theprocess chamber is an etch chamber; a plurality of sensors connected tothe process chamber; and a computing device connected to the processchamber and to each of the plurality of sensors, wherein the computingdevice is to: receive one or more measurements from at least one of theplurality of sensors during or after a process performed within theprocess chamber, wherein the process is an etch process, and wherein theone or more measurements comprise a reflectometry measurement of a filmon a substrate generated during the process; process the one or moremeasurements using a trained machine learning model, wherein the trainedmachine learning model is to generate an output based on processing ofthe one or more measurements, wherein the output comprises at least oneof an estimated film thickness or an estimated trench depth of the film;cause an action to be performed with respect to the process chamberbased on the output of the trained machine learning model, wherein theaction comprises stopping the etch process; determine a result of theaction, wherein the result of the action comprises at least one of a) adifference between a measured thickness of the film and the estimatedfilm thickness of the film orb) a difference between a measured trenchdepth of the film and the estimated trench depth of the film; and updatea training of the trained machine learning model based on the one ormore measurements, the output, and the result of the action.
 10. Theprocess tool of claim 9, wherein the trained machine learning modelcomprises a neural network.
 11. A process tool, comprising: a processchamber; a plurality of sensors connected to the process chamber; and acomputing device connected to the process chamber and to each of theplurality of sensors, wherein the computing device is to: receive one ormore measurements from at least one of the plurality of sensors duringor after a process performed within the process chamber, wherein theprocess comprises a seasoning process performed on the process chamberafter performing maintenance on the process chamber, and wherein the oneor more measurements comprise a set of measurements from the pluralityof sensors generated during the process; process the one or moremeasurements using a trained machine learning model, wherein the trainedmachine learning model is to generate an output based on processing ofthe one or more measurements, wherein the output comprises an indicationthat the process chamber is ready to be brought back into service; causean action to be performed with respect to the process chamber based onthe output of the trained machine learning model, wherein the actioncomprises a test process to be run on a test substrate in the processchamber; determine a result of the action, wherein the result of theaction comprises one or more measurements of the test substrategenerated during or after the test process; and update a training of thetrained machine learning model based on the one or more measurements,the output, and the result of the action.
 12. The process tool of claim11, wherein the set of measurements comprise optical measurements, powermeasurements, and pressure measurements.
 13. A substrate processingsystem, comprising: one or more transfer chambers; a plurality ofprocess chambers connected to the one or more transfer chambers, theplurality of process chambers comprising a first process chambercomprising a first plurality of sensors; and a computing deviceconnected to each of the plurality of process chambers, wherein thecomputing device is to: receive first measurements generated by thefirst plurality of sensors of the first process chamber during or aftera process is performed within the first process chamber; determine thatthe first process chamber is due for maintenance based on processing thefirst measurements from the first plurality of sensors using a firsttrained machine learning model; after maintenance has been performed onthe first process chamber, receive second measurements generated by thefirst plurality of sensors of the first process chamber during or aftera seasoning process is performed within the first process chamber;determine that the first process chamber is ready to be brought backinto service based on processing the second measurements from the firstplurality of sensors using a second trained machine learning model;cause a test process to be performed on a test substrate in the firstprocess chamber based on an output of the second trained machinelearning model; receive one or more chamber qualification results basedon measurements of the test substrate generated during or after the testprocess; and update a training of the second trained machine learningmodel based on the second measurements, the output, and the one or morechamber qualification results.
 14. The substrate processing system ofclaim 13, wherein the first measurements are generated during adeposition process or etch process performed on a substrate, and whereinthe second measurements are generated during a seasoning processperformed after the maintenance has been performed on the first processchamber.
 15. The substrate processing system of claim 13, wherein thefirst measurements and the second measurements each comprise opticalmeasurements, power measurements, and pressure measurements.
 16. Thesubstrate processing system of claim 13, wherein the computing device isfurther to: cause maintenance to be performed on the first processchamber based on an output of the first trained machine learning model;receive an indication as to whether the maintenance needed to beperformed on the first process chamber; and update a training of thefirst trained machine learning model based on the first measurements,the output, and the indication.
 17. The substrate processing system ofclaim 13, wherein the first process chamber is an etch chamber, whereinthe first measurements are generated during an etch process performed ona substrate, wherein the first measurements comprise a reflectometrymeasurement, and wherein the computing device is further to: determineat least one of an estimated film thickness or an estimated trench depthbased on processing the reflectometry measurement using a third trainedmachine learning model; make a comparison of at least one of a) theestimated film thickness to a target film thickness or b) the estimatedtrench depth to a target trench depth; determine, based on thecomparison, a time at which at least one of the target film thickness orthe target trench depth is estimated to be reached; and stop the etchprocess at the time at which at least one of the target film thicknessor the target trench depth is estimated to be reached.
 18. The substrateprocessing system of claim 13, wherein the first trained machinelearning model comprises a first neural network, and wherein the secondtrained machine learning model comprises a second neural network.